COVID-19 OUTBREAK: Under all circumstances, IEEE COINS 2020 proceedings will be published, and authors of accepted papers will have the option of presenting the paper remotely.


Supported and organized by IEEE Robotics and Automation Society, IEEE Council on Electronic Design Automation, IEEE Circuits and Systems Society, IEEE e-Health Technical Committee (ComSoc), IEEE Big Data Technical Committee (ComSoc), and IEEE IoT


The Artificial Intelligence, Machine Learning, and Advanced Analytics track at COINS encourages original and high-quality submissions related to one or more of the following topics (but not limited to):


A) Artificial intelligent and Machine learning Fundamentals:
Machine learning, artificial intelligence, and predictive analytics: analysis, modeling, simulation, and application in different domains
Platforms, architecture, and infrastructure for efficient data analytics
Data, Text, Stream, Process & Network Mining
Times Series Models
Bayesian Learning
Ensemble Learning
Transfer Learning
Reinforcement Learning
RNN, CNN & GAN
Markov-Chain & Monte-Carlo-Simulation
Datasets and Evaluation
Adaptive Systems
Generalization as search
Ontologies and Knowledge sharing
Brain-inspired representations learning
Business Intelligence and Data Mining techniques
Intelligent algorithms for Fog and cloud-based Internet of Things
Natural Language Processing
Image processing and Video Analytics

B) Big Data Analytics and Data Science:
Data, Text, Stream, Process & Network Mining
Big Data Analytics AdoptionBenefits of Big Data Analytics
Barriers to Big Data Analytics
Volume Growth of Analytic Big Data
Managing Analytic Big Data
Data Types for Big Data
Data Engineering Techniques
Collaborative Edge-Fog-Cloud Machine Learning Techniques
Role of Hadoop ecosystem in data analytics and Business Intelligence (BI)
Analysis data for visualization
Scalar visualization techniques
Framework for flow visualization
System aspects of visualization applications
Future trends in scientific visualization


C) Image Processing and Video Analytics:
3D computer vision
Action and behavior recognition
Biometrics, face, gesture, body pose
Image retrieval
Motion and tracking
Neural generative models, autoencoders, GANs
Recognition (object detection, categorization)
Representation learning, deep learning Scene analysis, and understanding
Segmentation, grouping, and shape
Transfer, low-shot, semi- and unsupervised learning
Video analysis and understanding: Vision + language, vision + other modalities
Vision applications and systems, vision for robotics and autonomous vehicles
Visual reasoning and logical representation

D) Speech Recognition and Understanding:
Automatic speech recognition
Spoken language understanding
Speech-to-text systems
Spoken dialog systems
Multilingual language processing
Robustness in automatic speech recognition
Spoken document retrieval
Speech-to-speech translation
Text-to-speech systems
Spontaneous speech processing
Speech summarization
New applications of automatic speech recognition